{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "<p>3279 rows × 1559 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0      1       2     3     4     5     6     7     8     9     ...  \\\n",
       "0     125.0  125.0  1.0000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "1      57.0  468.0  8.2105   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "2      33.0  230.0  6.9696   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3      60.0  468.0  7.8000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "4      60.0  468.0  7.8000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "...     ...    ...     ...   ...   ...   ...   ...   ...   ...   ...  ...   \n",
       "3274  170.0   94.0  0.5529   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3275  101.0  140.0  1.3861   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3276   23.0  120.0  5.2173   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3277    NaN    NaN     NaN   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "3278   40.0   40.0  1.0000   1.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   \n",
       "\n",
       "      1549  1550  1551  1552  1553  1554  1555  1556  1557  1558  \n",
       "0      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1  \n",
       "1      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1  \n",
       "2      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1  \n",
       "3      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1  \n",
       "4      0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     1  \n",
       "...    ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
       "3274   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "3275   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "3276   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "3277   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "3278   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0     0  \n",
       "\n",
       "[3279 rows x 1559 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "def convert_number(x):\n",
    "    try:\n",
    "        return float(x)\n",
    "    except ValueError:\n",
    "        return np.nan\n",
    "    \n",
    "converters = defaultdict()\n",
    "for i in range(1559 -1):\n",
    "    converters[i] = convert_number\n",
    "converters[1558] = lambda x:1 if x.strip() == \"ad.\" else 0\n",
    "\n",
    "ads_data = pd.read_csv(\"Data/ad.data\",header=None,converters=converters)\n",
    "ads_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2359, 1558)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = ads_data.dropna(axis=0,how='any')\n",
    "X = data.drop(1558,axis=1).values\n",
    "Y = data[1558]\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确度是：0.9393698945625968\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "dtc = DecisionTreeClassifier(random_state=14)\n",
    "score = cross_val_score(dtc, X, Y, scoring='accuracy')\n",
    "print(\"准确度是：{}\".format(score.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "# n_components 表示的组成分的数量\n",
    "pca = PCA(n_components=5)\n",
    "Xd = pca.fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.53634788e-01, 1.44730481e-01, 1.03263965e-03, 5.56149224e-05,\n",
       "       2.74925830e-05])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确度是：0.936405592140775\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "dtc = DecisionTreeClassifier(random_state=14)\n",
    "score = cross_val_score(dtc, Xd, Y, scoring='accuracy')\n",
    "print(\"准确度是：{}\".format(score.mean()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "data = ads_data.dropna(axis=0,how='any')\n",
    "Y = data[1558]\n",
    "classes = set(Y)\n",
    "colors = ['red', 'green']\n",
    "fig = plt.figure()\n",
    "ax = Axes3D(fig)\n",
    "\n",
    "for cur_class, color in zip(classes, colors):\n",
    "    mask = (Y == cur_class).values\n",
    "    ax.scatter(Xd[mask,0], Xd[mask,1],Xd[mask,2],color=color, label=int(cur_class),marker='o')\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
